Developing GPR model for forecasting the rock fragmentation in surface mines

Abstract

Blasting operation is an economical and common method for rock fragmentation in civil construction works, surface and underground mines. The aim of this study is to present an accurate model for predicting the rock fragmentation (D80) induced by blasting in Shur river dam region, Iran, through Gaussian process regression (GPR). For this aim, 72 blasting events were investigated and the values of six parameters, i.e. burden, spacing, stemming, powder factor, charge used per delay and D80 were measured. Firstly, 80% of the total data (58 datasets) were assigned to train the GPR, whereas the remaining 14 datasets were assigned to test the constructed GPR model. In GPR modeling, 5 different kernels, i.e. squared exponential, exponential, matern32, matern52 and rational quadratic, were employed. The proposed GPR models were then compared with the support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS) and hybrid ANFIS-particle swarm optimization (PSO). The results proved that the GPR-squared exponential model with the R-square (R 2) of 0.948 can forecast D80 better than the SVM with the R 2 of 0.83, ANFIS with the R 2 of 0.81 and ANFIS-PSO with the R 2 of 0.89.